{
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  "metadata": {
    "colab": {
      "name": "lasso_regression.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BknsqHEEqsI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83xnU_AyFGT9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "1501ef32-7099-4f1b-b853-e829c9c0c701"
      },
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import yfinance as yf\n",
        "import datetime\n",
        "start = datetime.datetime(2012,1,1)\n",
        "end= datetime.datetime(2018,8,30)\n",
        "yf.pdr_override()\n",
        "df_full = yf.download(\"jpm\",start = start,end=end).reset_index()\n",
        "df_full.to_csv('jpm.csv',index=False)\n",
        "df_full.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012-01-03</td>\n",
              "      <td>34.060001</td>\n",
              "      <td>35.189999</td>\n",
              "      <td>34.009998</td>\n",
              "      <td>34.980000</td>\n",
              "      <td>27.864342</td>\n",
              "      <td>44102800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>34.439999</td>\n",
              "      <td>35.150002</td>\n",
              "      <td>34.330002</td>\n",
              "      <td>34.950001</td>\n",
              "      <td>28.040850</td>\n",
              "      <td>36571200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>34.709999</td>\n",
              "      <td>35.919998</td>\n",
              "      <td>34.400002</td>\n",
              "      <td>35.680000</td>\n",
              "      <td>28.626539</td>\n",
              "      <td>38381400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-01-06</td>\n",
              "      <td>35.689999</td>\n",
              "      <td>35.770000</td>\n",
              "      <td>35.139999</td>\n",
              "      <td>35.360001</td>\n",
              "      <td>28.369787</td>\n",
              "      <td>33160600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-09</td>\n",
              "      <td>35.439999</td>\n",
              "      <td>35.680000</td>\n",
              "      <td>34.990002</td>\n",
              "      <td>35.299999</td>\n",
              "      <td>28.321667</td>\n",
              "      <td>23001800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date       Open       High        Low      Close  Adj Close    Volume\n",
              "0 2012-01-03  34.060001  35.189999  34.009998  34.980000  27.864342  44102800\n",
              "1 2012-01-04  34.439999  35.150002  34.330002  34.950001  28.040850  36571200\n",
              "2 2012-01-05  34.709999  35.919998  34.400002  35.680000  28.626539  38381400\n",
              "3 2012-01-06  35.689999  35.770000  35.139999  35.360001  28.369787  33160600\n",
              "4 2012-01-09  35.439999  35.680000  34.990002  35.299999  28.321667  23001800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SE2zgaEFFOUV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "92b4b3cc-45c8-4eef-ce8a-5bba35e0b87d"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('jpm.csv')\n",
        "df"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012-01-03</td>\n",
              "      <td>34.060001</td>\n",
              "      <td>35.189999</td>\n",
              "      <td>34.009998</td>\n",
              "      <td>34.980000</td>\n",
              "      <td>27.864342</td>\n",
              "      <td>44102800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>34.439999</td>\n",
              "      <td>35.150002</td>\n",
              "      <td>34.330002</td>\n",
              "      <td>34.950001</td>\n",
              "      <td>28.040850</td>\n",
              "      <td>36571200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>34.709999</td>\n",
              "      <td>35.919998</td>\n",
              "      <td>34.400002</td>\n",
              "      <td>35.680000</td>\n",
              "      <td>28.626539</td>\n",
              "      <td>38381400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-01-06</td>\n",
              "      <td>35.689999</td>\n",
              "      <td>35.770000</td>\n",
              "      <td>35.139999</td>\n",
              "      <td>35.360001</td>\n",
              "      <td>28.369787</td>\n",
              "      <td>33160600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-09</td>\n",
              "      <td>35.439999</td>\n",
              "      <td>35.680000</td>\n",
              "      <td>34.990002</td>\n",
              "      <td>35.299999</td>\n",
              "      <td>28.321667</td>\n",
              "      <td>23001800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1671</th>\n",
              "      <td>2018-08-23</td>\n",
              "      <td>114.959999</td>\n",
              "      <td>115.150002</td>\n",
              "      <td>114.430000</td>\n",
              "      <td>114.730003</td>\n",
              "      <td>109.777924</td>\n",
              "      <td>9265400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1672</th>\n",
              "      <td>2018-08-24</td>\n",
              "      <td>114.980003</td>\n",
              "      <td>115.220001</td>\n",
              "      <td>114.449997</td>\n",
              "      <td>114.680000</td>\n",
              "      <td>109.730087</td>\n",
              "      <td>8845700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1673</th>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>115.220001</td>\n",
              "      <td>117.279999</td>\n",
              "      <td>115.169998</td>\n",
              "      <td>116.709999</td>\n",
              "      <td>111.672455</td>\n",
              "      <td>13768000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1674</th>\n",
              "      <td>2018-08-28</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>117.029999</td>\n",
              "      <td>115.970001</td>\n",
              "      <td>116.139999</td>\n",
              "      <td>111.127060</td>\n",
              "      <td>8302600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1675</th>\n",
              "      <td>2018-08-29</td>\n",
              "      <td>116.349998</td>\n",
              "      <td>116.370003</td>\n",
              "      <td>115.360001</td>\n",
              "      <td>115.760002</td>\n",
              "      <td>110.763466</td>\n",
              "      <td>7221700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1676 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date        Open        High  ...       Close   Adj Close    Volume\n",
              "0     2012-01-03   34.060001   35.189999  ...   34.980000   27.864342  44102800\n",
              "1     2012-01-04   34.439999   35.150002  ...   34.950001   28.040850  36571200\n",
              "2     2012-01-05   34.709999   35.919998  ...   35.680000   28.626539  38381400\n",
              "3     2012-01-06   35.689999   35.770000  ...   35.360001   28.369787  33160600\n",
              "4     2012-01-09   35.439999   35.680000  ...   35.299999   28.321667  23001800\n",
              "...          ...         ...         ...  ...         ...         ...       ...\n",
              "1671  2018-08-23  114.959999  115.150002  ...  114.730003  109.777924   9265400\n",
              "1672  2018-08-24  114.980003  115.220001  ...  114.680000  109.730087   8845700\n",
              "1673  2018-08-27  115.220001  117.279999  ...  116.709999  111.672455  13768000\n",
              "1674  2018-08-28  117.000000  117.029999  ...  116.139999  111.127060   8302600\n",
              "1675  2018-08-29  116.349998  116.370003  ...  115.760002  110.763466   7221700\n",
              "\n",
              "[1676 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoA46lkJFRRH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "aeb7fe9b-a0cd-4f57-fb67-d5fc3659d6b1"
      },
      "source": [
        "close_px = df['Adj Close']\n",
        "mavg = close_px.rolling(window=100).mean()\n",
        "mavg"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              NaN\n",
              "1              NaN\n",
              "2              NaN\n",
              "3              NaN\n",
              "4              NaN\n",
              "           ...    \n",
              "1671    105.630344\n",
              "1672    105.676721\n",
              "1673    105.728722\n",
              "1674    105.801820\n",
              "1675    105.858816\n",
              "Name: Adj Close, Length: 1676, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vk-AL5u4FU3r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "7bf22cdc-6f47-4b94-beeb-963d9ee9f486"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import style\n",
        "\n",
        "# Adjusting the size of matplotlib\n",
        "import matplotlib as mpl\n",
        "mpl.rc('figure', figsize=(8, 7))\n",
        "mpl.__version__\n",
        "\n",
        "# Adjusting the style of matplotlib\n",
        "style.use('ggplot')\n",
        "\n",
        "close_px.plot(label='AAPL')\n",
        "mavg.plot(label='mavg')\n",
        "plt.legend()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbc55745748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KKYyLvobyd+Pyf/8Fl8PkU2BkHgzN8m1UkDk8cMxruyooqmni+mnDcNgG7rtq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\n+yP5qQohRLTzeMBmQ7Uyzvd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6YpZvKWXO6FTumjuCBIf8yhVdI++shRjA9K7PrIVD+yG0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\n56G+/rDqIYQQwpsE617EHagBGho63iH/AAAqLrH1sbRm9a5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uavo+qX9kl8sshBCib/Pr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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDCC88yEFYYM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "cafcf6ad-67e9-4b7d-bae6-7008acbf50e8"
      },
      "source": [
        "dfcomp = yf.download(['jpm','AAPL', 'GE', 'GOOG', 'IBM', 'MSFT'],start = start,end=end)['Adj Close']\n",
        "dfcomp"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  6 of 6 completed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>JPM</th>\n",
              "      <th>MSFT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-01-03</th>\n",
              "      <td>50.994907</td>\n",
              "      <td>13.639091</td>\n",
              "      <td>331.462585</td>\n",
              "      <td>139.934006</td>\n",
              "      <td>27.864342</td>\n",
              "      <td>22.020796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>51.268970</td>\n",
              "      <td>13.787668</td>\n",
              "      <td>332.892242</td>\n",
              "      <td>139.363144</td>\n",
              "      <td>28.040850</td>\n",
              "      <td>22.539021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>51.838169</td>\n",
              "      <td>13.780239</td>\n",
              "      <td>328.274536</td>\n",
              "      <td>138.702209</td>\n",
              "      <td>28.626539</td>\n",
              "      <td>22.769344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>52.380054</td>\n",
              "      <td>13.854527</td>\n",
              "      <td>323.796326</td>\n",
              "      <td>137.109772</td>\n",
              "      <td>28.369787</td>\n",
              "      <td>23.123066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>52.296970</td>\n",
              "      <td>14.010525</td>\n",
              "      <td>310.067780</td>\n",
              "      <td>136.396225</td>\n",
              "      <td>28.321667</td>\n",
              "      <td>22.818701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-08-23</th>\n",
              "      <td>211.061111</td>\n",
              "      <td>11.874558</td>\n",
              "      <td>1205.380005</td>\n",
              "      <td>135.556732</td>\n",
              "      <td>109.777924</td>\n",
              "      <td>105.249374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-08-24</th>\n",
              "      <td>211.717331</td>\n",
              "      <td>11.836680</td>\n",
              "      <td>1220.650024</td>\n",
              "      <td>136.181503</td>\n",
              "      <td>109.730087</td>\n",
              "      <td>106.071335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-08-27</th>\n",
              "      <td>213.460739</td>\n",
              "      <td>12.092353</td>\n",
              "      <td>1241.819946</td>\n",
              "      <td>136.787628</td>\n",
              "      <td>111.672455</td>\n",
              "      <td>107.245552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-08-28</th>\n",
              "      <td>215.184570</td>\n",
              "      <td>12.082885</td>\n",
              "      <td>1231.150024</td>\n",
              "      <td>136.694382</td>\n",
              "      <td>111.127060</td>\n",
              "      <td>107.891388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-08-29</th>\n",
              "      <td>218.397171</td>\n",
              "      <td>12.281741</td>\n",
              "      <td>1249.300049</td>\n",
              "      <td>137.580231</td>\n",
              "      <td>110.763466</td>\n",
              "      <td>109.613571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1676 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  AAPL         GE  ...         JPM        MSFT\n",
              "Date                               ...                        \n",
              "2012-01-03   50.994907  13.639091  ...   27.864342   22.020796\n",
              "2012-01-04   51.268970  13.787668  ...   28.040850   22.539021\n",
              "2012-01-05   51.838169  13.780239  ...   28.626539   22.769344\n",
              "2012-01-06   52.380054  13.854527  ...   28.369787   23.123066\n",
              "2012-01-09   52.296970  14.010525  ...   28.321667   22.818701\n",
              "...                ...        ...  ...         ...         ...\n",
              "2018-08-23  211.061111  11.874558  ...  109.777924  105.249374\n",
              "2018-08-24  211.717331  11.836680  ...  109.730087  106.071335\n",
              "2018-08-27  213.460739  12.092353  ...  111.672455  107.245552\n",
              "2018-08-28  215.184570  12.082885  ...  111.127060  107.891388\n",
              "2018-08-29  218.397171  12.281741  ...  110.763466  109.613571\n",
              "\n",
              "[1676 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2A201lhUFc_3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "4cb7bc13-c5a9-4d51-b050-200148706a16"
      },
      "source": [
        "retscomp = dfcomp.pct_change()\n",
        "\n",
        "corr = retscomp.corr()\n",
        "corr"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>JPM</th>\n",
              "      <th>MSFT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.231933</td>\n",
              "      <td>0.358248</td>\n",
              "      <td>0.284489</td>\n",
              "      <td>0.302823</td>\n",
              "      <td>0.361851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>0.231933</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.311084</td>\n",
              "      <td>0.413162</td>\n",
              "      <td>0.469752</td>\n",
              "      <td>0.313180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GOOG</th>\n",
              "      <td>0.358248</td>\n",
              "      <td>0.311084</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.338729</td>\n",
              "      <td>0.365240</td>\n",
              "      <td>0.486786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBM</th>\n",
              "      <td>0.284489</td>\n",
              "      <td>0.413162</td>\n",
              "      <td>0.338729</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.428698</td>\n",
              "      <td>0.408897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JPM</th>\n",
              "      <td>0.302823</td>\n",
              "      <td>0.469752</td>\n",
              "      <td>0.365240</td>\n",
              "      <td>0.428698</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.423728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.361851</td>\n",
              "      <td>0.313180</td>\n",
              "      <td>0.486786</td>\n",
              "      <td>0.408897</td>\n",
              "      <td>0.423728</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          AAPL        GE      GOOG       IBM       JPM      MSFT\n",
              "AAPL  1.000000  0.231933  0.358248  0.284489  0.302823  0.361851\n",
              "GE    0.231933  1.000000  0.311084  0.413162  0.469752  0.313180\n",
              "GOOG  0.358248  0.311084  1.000000  0.338729  0.365240  0.486786\n",
              "IBM   0.284489  0.413162  0.338729  1.000000  0.428698  0.408897\n",
              "JPM   0.302823  0.469752  0.365240  0.428698  1.000000  0.423728\n",
              "MSFT  0.361851  0.313180  0.486786  0.408897  0.423728  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emt_IgD8Fges",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "dfreg = df.loc[:,['Adj Close','Volume']]\n",
        "dfreg['HL_PCT'] = (df['High']-df['Low']) / df['Close'] * 100.0\n",
        "dfreg['PCT_change'] = (df['Close']-df['Open']) / df['Open'] * 100.0\n",
        "dfreg.fillna(value=-99999, inplace=True)\n",
        "forecast_out = int(math.ceil(0.01 * len(dfreg)))\n",
        "forecast_col = 'Adj Close'\n",
        "dfreg['label'] = dfreg[forecast_col].shift(-forecast_out)\n",
        "X = np.array(dfreg.drop(['label'], 1))\n",
        "X = preprocessing.scale(X)\n",
        "X_lately = X[-forecast_out:]\n",
        "X = X[:-forecast_out]\n",
        "y = np.array(dfreg['label'])\n",
        "y = y[:-forecast_out]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WeqmxZMFpnr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import Lasso\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lUYeEpQF3Eb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clflasso = Lasso()\n",
        "clflasso.fit(X_train, y_train)\n",
        "confidencelasso = clflasso.score(X_test,y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlSop_ESF61C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d4fcadeb-8b01-4c8e-f841-2c7f56898d6b"
      },
      "source": [
        "print(\"The Accuracy of our Model is %r\" %confidencelasso)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Accuracy of our Model is 0.9806857462238061\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQEzaQ8NGH71",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "5db69305-95ff-4dfa-e1d5-4e0b5d023aee"
      },
      "source": [
        "forecast_set =clflasso.predict(X_lately)\n",
        "dfreg['Forecast'] = np.nan\n",
        "forecast_set"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([111.34130043, 111.5623028 , 110.72433671, 109.66539761,\n",
              "       107.97107304, 108.67090898, 107.79611405, 108.78140282,\n",
              "       108.78140282, 108.64327267, 109.28785802, 108.96556167,\n",
              "       108.74456664, 108.6985306 , 110.56779948, 110.04292987,\n",
              "       109.69301924])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToRQqQbgGMRX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "fa4b99e6-83da-45ed-8867-4dff5cd6afbc"
      },
      "source": [
        "next_unix =  datetime.datetime.now() + datetime.timedelta(days=1)\n",
        "\n",
        "for i in forecast_set:\n",
        "    next_date = next_unix\n",
        "    next_unix += datetime.timedelta(days=1)\n",
        "    dfreg.loc[next_date] = [np.nan for _ in range(len(dfreg.columns)-1)]+[i]\n",
        "dfreg['Adj Close'].tail(500).plot()\n",
        "dfreg['Forecast'].tail(500).plot()\n",
        "plt.legend(loc=4)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AdLuz3WsEZrP7U5aF+tAVES1zVOnOpD2/yXvWZcHz5k+UWrEK69pbyVx1Jh11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\nEgEU+CIiIhHg/wCRSwDydCeP4QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahKjg6KyGYnd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
